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This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.
IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018
Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.
Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Impartido por:

Romeo Kienzler

Chief Data Scientist, Course Lead

Niketan Pansare

Senior Software Engineer

Tom Hanlon

Training Director

Max Pumperla

Deep Learning Engineer

Ilja Rasin

Data Scientist

Transcripción

In this video, we're going to talk about more general, non-sequential models in Keras using the functional API. So there is two types of models in Keras. We've seen the sequential already. There is another type called model, which you would use if you were in need of non-sequential models. Once defined, and we will see how to do that in just a second. The model can be trained and evaluated exactly like sequential models. So you don't have to learn anything about that part. Using the functional API, you would usually start with model and configuring one or several inputs. Once you have those inputs defined, we define transformations for those inputs, until you arrive at one or several outputs. So let's look at an example. This is an example we kind of have already seen using the sequential model. Mainly we do predictions of 100 predictions using amnest. We've got two layers here first. First one is Dense time layers which we have already seen and the second one is a new layer type called Input. On top of that, we import our new model class. As before, we define the number of classes as 10 because we have 10 digits here. And we define an input layer of shape 784, which is of vector shape length 784. Then what we do next is new, we define a Dense of output dimension 512, but instead of simply defining it, we call it on our inputs. So we define x as a Dense layer applied to our inputs. So what that means is, every layer in Keras, actually, every model for that purpose can be called in tensors to output tensors, and we do that here. So this is part of the functional API. We can do this step again and apply our intermediary x to a Dense layer again to arrive at yet another x. Finally, we can do this a third time, this time with the number of classes as output dimension to arrive at our predictions. So this procedure of calling layers on different inputs, you can do that in any fashion, and slowly build up very complicated models. Now, to initialize and run a model is quite simple, you simply specify the inputs and the outputs, that's it. Compilation step, fitting, evaluating and so on, it's just the same thing as before.